In this paper, we are interested in qualifying uncertainty to solve OOD detection in text classification as it contains a wide range of Natural Language Processing (NLP) applications [1]}, [2]}.
Although fine-tuning pre-trained transformers [3]}
have achieved state-of-the-art accuracy on text classification tasks, they still suffer from the same over-confidence problem of traditional neural networks, making the prediction untrustful [4]}.
One partial explanation is over-parameterization [5]}.
Although transformers are pre-trained on a large corpus and get rich semantic information, it leads to over-confidence easily given limited labeled data during the fine-tuning stage [6]}.
Overall, compared to the Computer Vision (CV) domain, there is less work in qualifying uncertainty in the NLP domain. Among them, there are Bayesian and non-Bayesian methods.
